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Record W2137994134 · doi:10.18806/tesl.v29i1.1094

Computer Language Settings and Canadian Spellings

2012· article· en· W2137994134 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
venuePublished in a venue whose home country is Canada.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

Bibliographic record

VenueTESL Canada Journal · 2012
Typearticle
Languageen
FieldComputer Science
TopicDigital Communication and Language
Canadian institutionsThinkpath Engineering Services (Canada)
Fundersnot available
KeywordsSpellingSpellSet (abstract data type)Computer scienceFlaggingLinguisticsWord (group theory)English languageAmerican EnglishWord processingFirst languageNatural language processingHistoryProgramming languageSociology

Abstract

fetched live from OpenAlex

The language settings used on personal computers interact with the spell-checker in Microsoft Word, which directly affects the flagging of spellings that are deemed incorrect. This study examined the language settings of personal computers owned by a group of Canadian university students. Of 21 computers examined, only eight had their Windows “Default Input Language” set to English (Canada); the remainder had it set to English (United States). Furthermore, only eight of the computers had the Microsoft Word “Primary Editing Language” set to English (Canada), whereas 11 had it set to English (United States). When asked to state their preferred spelling for words where the spelling differs between Canadian English and American English, a significant proportion of students preferred American spellings for some words. The study indicates that computer language settings may contribute to the increasing use of American spellings among Canadian students. The implications for ESL teaching are discussed.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.526
Threshold uncertainty score0.811

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.007
GPT teacher head0.201
Teacher spread0.194 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it